Personalized Federated Learning via ADMM with Moreau Envelope

Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergence on heterogeneous data. However, most existing PFL frameworks require strong assumptions for convergence. In this paper, we propose an alternating direction method of multipliers (ADMM) for training PFL models with Moreau envelope (FLAME), which achieves a sublinear convergence rate, relying on the relatively weak assumption of gradient Lipschitz continuity. Moreover, due to the gradient-free nature of ADMM, FLAME alleviates the need for hyperparameter tuning, particularly in avoiding the adjustment of the learning rate when training the global model. In addition, we propose a biased client selection strategy to expedite the convergence of training of PFL models. Our theoretical analysis establishes the global convergence under both unbiased and biased client selection strategies. Our experiments validate that FLAME, when trained on heterogeneous data, outperforms state-of-the-art methods in terms of model performance. Regarding communication efficiency, it exhibits an average speedup of 3.75x compared to the baselines. Furthermore, experimental results validate that the biased client selection strategy speeds up the convergence of both personalized and global models.Comment: 15 page

CTLs heterogeneity and plasticity: implications for cancer immunotherapy

Abstract Cytotoxic T lymphocytes (CTLs) play critical antitumor roles, encompassing diverse subsets including CD4+, NK, and γδ T cells beyond conventional CD8+ CTLs. However, definitive CTLs biomarkers remain elusive, as cytotoxicity-molecule expression does not necessarily confer cytotoxic capacity. CTLs differentiation involves transcriptional regulation by factors such as T-bet and Blimp-1, although epigenetic regulation of CTLs is less clear. CTLs promote tumor killing through cytotoxic granules and death receptor pathways, but may also stimulate tumorigenesis in some contexts. Given that CTLs cytotoxicity varies across tumors, enhancing this function is critical. This review summarizes current knowledge on CTLs subsets, biomarkers, differentiation mechanisms, cancer-related functions, and strategies for improving cytotoxicity. Key outstanding questions include refining the CTLs definition, characterizing subtype diversity, elucidating differentiation and senescence pathways, delineating CTL-microbe relationships, and enabling multi-omics profiling. A more comprehensive understanding of CTLs biology will facilitate optimization of their immunotherapy applications. Overall, this review synthesizes the heterogeneity, regulation, functional roles, and enhancement strategies of CTLs in antitumor immunity, highlighting gaps in our knowledge of subtype diversity, definitive biomarkers, epigenetic control, microbial interactions, and multi-omics characterization. Addressing these questions will refine our understanding of CTLs immunology to better leverage cytotoxic functions against cancer

Psychiatric disorders associated with immune checkpoint inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) databaseResearch in context

Summary: Background: With the increasing use of immune checkpoint inhibitors (ICIs) for tumour immunotherapy, the immune-related adverse events (irAEs) caused by their collateral effect on the immune system pose a key challenge for the clinical application of ICIs. Psychiatric adverse events are a class of adverse events associated with ICIs that are realistically observed in the real world. We aim to provide a comprehensive study and summary of psychiatric adverse events associated with ICIs. Methods: We obtained ICI adverse reaction reports during January 2012–December 2021 from the FDA Adverse Event Reporting System (FAERS) database. ICI reports underwent screening to minimize the influence of other adverse reactions, concomitant medications, and indications for medication use that may also contribute to psychiatric disorders. Disproportionality analysis was performed to find psychiatric adverse events associated with ICIs by comparing ICIs with the full FAERS database using the reporting odds ratio (ROR). Influencing factors were explored based on univariate logistic regression analysis. Finally, the Cancer Genome Atlas (TCGA) pan-cancer transcriptome data were combined to explore the potential biological mechanisms associated with ICI-related pAEs. Findings: Reports of psychiatric adverse events accounted for 2.71% of the overall ICI adverse event reports in the FAERS database. Five categories of psychiatric adverse events were defined as ICI-related psychiatric adverse events (pAEs). The median age of reports with ICI-related pAEs was 70 (interquartile range [IQR] 24–95), with 21.54% of reports having a fatal outcome. Cases with indications for lung cancer, skin cancer and kidney site cancer accounted for the majority. The odds of ICI-related pAEs increased in older patients (65–74: OR = 1.44 [1.22–1.70], P < 0.0001: ≥75: OR = 1.84 [1.54–2.20], P < 0.0001). The occurrence of ICI-related pAEs may be related to NOTCH signalling and dysregulation of synapse-associated pathways. Interpretation: This study investigated psychiatric adverse events highly associated with ICI treatment, their influencing factors and potential biological mechanisms, which provides a reliable basis for further in-depth study of ICI-related pAEs. However, as an exploratory study, our findings need to be further confirmed in a large-scale prospective study. Funding: This work was supported by the Natural Science Foundation of Guangdong Province (2018A030313846 and 2021A1515012593), the Science and Technology Planning Project of Guangdong Province (2019A030317020) and the National Natural Science Foundation of China (81802257, 81871859, 81772457, 82172750 and 82172811). Guangdong Basic and Applied Basic Research Foundation (Guangdong - Guangzhou Joint Fouds) (2022A1515111212). This work was supported by Key Research and Development Projects of Sichuan Science and Technology (2022YFS0221, 2022YFS0074, 2022YFS0156 and 2022YFS0378). Sichuan Provincial People's Hospital Hospital Young Talent Fund (2021QN08)

Research prospects for kidney xenotransplantation: a bibliometric analysis

Background Xenograft kidney transplantation has been receiving increasing attention. The purpose of this study is to use bibliometric analysis to identify papers in this research field and explore their current status and development trends.Methods Using the data in the Web of Science core database from Clarivate Analytics as the object of study, we used ‘TS = Kidney OR Renal AND xenotransplantation’ as the search term to find all literature from 1980 to 2 November 2022.Results In total, 1005 articles were included. The United States has the highest number of publications and has made significant contributions in this field. Harvard University was at the forefront of this study. Professor Cooper has published 114 articles in this field. Xenotransplantation has the largest number of relevant articles. Transplantation was the most cited journal. High-frequency keywords illustrated the current state of development and future trends in xenotransplantation. The use of transgenic pigs and the development of coordinated co-stimulatory blockers have greatly facilitated progress in xenotransplantation research. We found that ‘co-stimulation blockade’, ‘xenograft survival’, ‘pluripotent stem cell’, ‘translational research’, and ‘genetic engineering’ were likely to be the focus of attention in the coming years.Conclusions This study screened global publications related to xenogeneic kidney transplantation; analyzed their literature metrology characteristics; identified the most cited articles in the research field; understood the current situation, hot spots, and trends of global research; and provided future development directions for researchers and practitioners

Identification of Breast-Cancer-Associated Properties of Drinking Water under a Composite-Toxicity Perspective of Mixed Contaminants: A Case Study in a High-Prevalence Area of China

Breast cancer is the most frequently diagnosed female cancer worldwide. Environmental contaminant exposure is suspected to be crucial, but the broad-spectrum communal properties that these suspected contaminants all share remain to be explored, especially in source and drinking water. In this work, we focused on the Pearl River Basin, which has the highest breast cancer incidence and mortality in China, and hypothesized that the breast cancer risk in this area is associated with its water source. Our objective was to resolve the possible communal properties that are associated with breast cancer from water mixture extracts of source and drinking water and to identify the key drivers by utilizing the latest epidemiology data, performing an exhaustive water toxicological and chemical characterization, and combining partial least-squares path statistics modeling (PLS-PM). We proposed a path for a drinking water-toxicity-induced breast cancer risk and confirmed its association with estrogen-receptor- and thiol-depletion-relevant mechanisms. The breast cancer incidence risk was associated with water-mixture-promoted mammalian cell proliferation (i.e., estrogenic effect), while the mortality risk was associated with a greater thiol depletion (i.e., oxidative stress). Endocrine-disrupting chemicals (EDCs) and dissolved organic matter (DOM) from anthropogenic sources in drinking water are key drivers for estrogenic effects and oxidative stress, respectively. The PLS-PM standardized effects of the DOM and EDCs in treated water on the breast cancer incidence and mortality were −0.07 and 0.31, and 0.35 and 0.31, respectively, further revealing that EDCs strongly influence the incidence risk, whereas the mortality risk resulted from the joint effects of EDCs and DOM. This study clearly shows an association between the breast cancer risk and drinking water toxicity in a high-prevalence area of China, broadening the future perspectives for water-contaminant-specific breast cancer prevention research

Table1_Case Report: Diagnosis of vertebral alveolar echinococcosis upon next-generation sequencing in a suspected tuberculosis.docx

IntroductionAlveolar echinococcosis (AE), caused by larval stages of Echinococcus multilocularis, is a rare zoonotic disease that mainly involves the liver. The diagnosis of extrahepatic AE is usually difficult. Here, we describe a rare case of vertebral alveolar echinococcosis with a suspected history of spinal tuberculosis, diagnosed by metagenomic next-generation sequencing (mNGS).Case PresentationA 44-year-old woman presented with repetitive neck and back pain, with a surgical history of suspected spinal tuberculosis. Magnetic resonance imaging (MRI) showed cystic masses in the craniocervical junction region and effusion around lumbar vertebrae. Multiple culture tests were performed to detect tuberculosis and other pathogens through puncture of the effusion and of cerebrospinal fluid, but the results were all negative. Finally, mNGS of the effusion fluid was performed and Echinococcus multilocularis were detected. The results were further confirmed by Sanger sequencing.ConclusionThis case emphasizes a role of mNGS in the diagnosis of infectious diseases with unknown pathogen. As a newly emerged sensitive and accurate diagnostic strategy, mNGS provides clinicians an opportunity to clarify pathogens in complicated infectious cases, especially in patients with a history of multiple infections.</p

Image2_Case Report: Diagnosis of vertebral alveolar echinococcosis upon next-generation sequencing in a suspected tuberculosis.tif

IntroductionAlveolar echinococcosis (AE), caused by larval stages of Echinococcus multilocularis, is a rare zoonotic disease that mainly involves the liver. The diagnosis of extrahepatic AE is usually difficult. Here, we describe a rare case of vertebral alveolar echinococcosis with a suspected history of spinal tuberculosis, diagnosed by metagenomic next-generation sequencing (mNGS).Case PresentationA 44-year-old woman presented with repetitive neck and back pain, with a surgical history of suspected spinal tuberculosis. Magnetic resonance imaging (MRI) showed cystic masses in the craniocervical junction region and effusion around lumbar vertebrae. Multiple culture tests were performed to detect tuberculosis and other pathogens through puncture of the effusion and of cerebrospinal fluid, but the results were all negative. Finally, mNGS of the effusion fluid was performed and Echinococcus multilocularis were detected. The results were further confirmed by Sanger sequencing.ConclusionThis case emphasizes a role of mNGS in the diagnosis of infectious diseases with unknown pathogen. As a newly emerged sensitive and accurate diagnostic strategy, mNGS provides clinicians an opportunity to clarify pathogens in complicated infectious cases, especially in patients with a history of multiple infections.</p

Machine learning-guided design and development of metallic structural materials

In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

BackgroundThis study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.MethodsIn total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.ResultsThere were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation&nbsp;vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].ConclusionOur combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma